Block 3 Data Analysis
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.3.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(lubridate)
## Warning: package 'lubridate' was built under R version 3.3.3
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.3.3
library(plotly)
## Warning: package 'plotly' was built under R version 3.3.3
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(ggforce)
## Warning: package 'ggforce' was built under R version 3.3.3
library(sqldf)
## Warning: package 'sqldf' was built under R version 3.3.3
## Loading required package: gsubfn
## Warning: package 'gsubfn' was built under R version 3.3.3
## Loading required package: proto
## Warning: package 'proto' was built under R version 3.3.3
## Loading required package: RSQLite
## Warning: package 'RSQLite' was built under R version 3.3.3
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.3.3
block3 <- read.csv("users_pw_anm_block3.csv", stringsAsFactors = FALSE,
na.strings=c("NULL","NULL "," NULL ","NA"," ","", "-1"))
str(block3)
## 'data.frame': 3622 obs. of 95 variables:
## $ user_id : chr "238" "238" "238" "238" ...
## $ user_type : chr "1" "1" "1" "1" ...
## $ user_name : chr "Alka Santosh Nikam" "Alka Santosh Nikam" "Alka Santosh Nikam" "Alka Santosh Nikam" ...
## $ user_sex : chr "1" "1" "1" "1" ...
## $ user_aadhar_id : num 7.3e+11 7.3e+11 7.3e+11 7.3e+11 7.3e+11 ...
## $ user_login_pin : int 3165 3165 3165 3165 3165 3165 3165 NA 3165 3165 ...
## $ user_mobile_no : num 9.88e+09 9.88e+09 9.88e+09 9.88e+09 9.88e+09 ...
## $ block_id : int 3 3 3 3 3 3 3 NA 3 3 ...
## $ center_id : int 54 54 54 54 54 54 54 NA 54 54 ...
## $ subcenter_id : int 413 413 413 413 413 413 413 NA 413 413 ...
## $ user_device_id : chr "6e9d9846fe3c0c2e" "6e9d9846fe3c0c2e" "6e9d9846fe3c0c2e" "6e9d9846fe3c0c2e" ...
## $ user_imei_no : chr "353330066306915" "353330066306915" "353330066306915" "353330066306915" ...
## $ registeration_date : chr "2017-02-18 11:42:18" "2017-02-18 11:42:18" "2017-02-18 11:42:18" "2017-02-18 11:42:18" ...
## $ user_acc_status : int 1 1 1 1 1 1 1 NA 1 1 ...
## $ fcm_registered_id : chr "fA1j9H_V-rw:APA91bFLelJwkJDwvikESW6CPHy5PYYKk1GUmgj1Ldb-qv5PWzbG9IZxffIE7dqfk5i3CAnn6aehVtZ7hpTtttxXZEL7XjHo7kwaJnMyUYIPfHPeUZv"| __truncated__ "fA1j9H_V-rw:APA91bFLelJwkJDwvikESW6CPHy5PYYKk1GUmgj1Ldb-qv5PWzbG9IZxffIE7dqfk5i3CAnn6aehVtZ7hpTtttxXZEL7XjHo7kwaJnMyUYIPfHPeUZv"| __truncated__ "fA1j9H_V-rw:APA91bFLelJwkJDwvikESW6CPHy5PYYKk1GUmgj1Ldb-qv5PWzbG9IZxffIE7dqfk5i3CAnn6aehVtZ7hpTtttxXZEL7XjHo7kwaJnMyUYIPfHPeUZv"| __truncated__ "fA1j9H_V-rw:APA91bFLelJwkJDwvikESW6CPHy5PYYKk1GUmgj1Ldb-qv5PWzbG9IZxffIE7dqfk5i3CAnn6aehVtZ7hpTtttxXZEL7XjHo7kwaJnMyUYIPfHPeUZv"| __truncated__ ...
## $ user_app_version : chr "2.2.1" "2.2.1" "2.2.1" "2.2.1" ...
## $ last_login_time : chr "2017-09-30 16:21:07" "2017-09-30 16:21:07" "2017-09-30 16:21:07" "2017-09-30 16:21:07" ...
## $ pw_profile_id : int 351 566 581 588 688 803 1147 NA 1148 1327 ...
## $ qrcode : chr "NK-DVL-LHR-2017-A-076" "NK-DVL-LHR-2017-A-077" "NK-DVL-LHR-2017-A-078" "NK-DVL-LHR-2017-A-079" ...
## $ pw_profile_name : chr "JYa Ganesh SonWNe" "Jayshri Manohar chuare" "Farat Liyakat Shekh" "Pratibha Dinesh Chadrima" ...
## $ pw_profile_dob : chr "0000-00-00" "0000-00-00" "0000-00-00" "0000-00-00" ...
## $ pw_profile_age : chr "26" "28" "26" "24" ...
## $ pw_profile_address : chr "Deola (MalegaonRpad)" "Duulatnagar Deola" "Deola" "Deola" ...
## $ subcenter_id.1 : chr "413" "413" "413" "413" ...
## $ pw_profile_landmark : chr "walhari road" "Omnagar" "Hudaki" "Daulatnagar" ...
## $ pw_profile_contact_no : chr "9096815085" "9765090997" "7741091259" "8007444491" ...
## $ pw_profile_bpl_card : chr "1" "0" "0" "0" ...
## $ pw_profile_edu : num 1 5 3 6 3 4 4 NA 2 3 ...
## $ pw_profile_type_uid : int 4 2 4 4 2 2 4 NA 2 4 ...
## $ pw_profile_uid_no : num NA 1.27e+11 NA NA 1.27e+11 ...
## $ pw_profile_lmp : chr "2016-07-06" "2016-08-21" "2016-10-25" "2016-07-11" ...
## $ pw_profile_edd : chr "2017-04-12" "2017-05-28" "2017-08-01" "2017-04-17" ...
## $ pw_profile_first_preg : chr "0" "0" "0" "1" ...
## $ pw_profile_birth_order : chr "2" "1" "2" NA ...
## $ pw_profile_prev_pih : int 0 0 0 NA NA NA NA NA 0 0 ...
## $ pw_profile_prev_sb : int 0 0 0 NA NA NA NA NA 0 0 ...
## $ pw_profile_prev_low_wt : int 0 0 0 NA NA NA NA NA 0 0 ...
## $ pw_profile_prev_miscarriage : int 0 0 0 NA NA NA NA NA 0 0 ...
## $ pw_profile_prev_c_section : int 0 1 0 NA NA NA NA NA 0 0 ...
## $ pw_profile_prev_pph : int 0 0 0 NA NA NA NA NA 0 0 ...
## $ pw_profile_diabetes : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_kidney_disease : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_heart_disease : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_thyroid_disease : int 0 0 0 0 0 0 1 NA 0 0 ...
## $ pw_profile_epilepsy : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_reproductive_surg : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_abdominal_surg : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_other_flag : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_other : chr NA NA NA NA ...
## $ pw_profile_family_diabetes : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_family_pih : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ pw_profile_multi_preg : int 0 0 0 0 0 0 0 NA 9 0 ...
## $ pw_profile_blood_group : int 4 4 4 4 1 2 3 NA 2 2 ...
## $ pw_profile_hiv : int 0 0 0 0 0 0 0 NA 9 0 ...
## $ pw_profile_gps_lat : num NA NA NA NA NA NA NA NA NA NA ...
## $ pw_profile_gps_long : num NA NA NA NA NA NA NA NA NA NA ...
## $ pw_profile_form_id : chr "6e9d9846fe3c0c2e_2017-03-09 13:19:44_ver1.0.7" "6e9d9846fe3c0c2e_2017-03-13 14:10:34_ver1.0.7" "6e9d9846fe3c0c2e_2017-03-14 11:35:15_ver1.0.7" "6e9d9846fe3c0c2e_2017-03-14 11:53:46_ver1.0.7" ...
## $ user_id.1 : chr "238" "238" "238" "238" ...
## $ pw_profile_form_start_time : chr "2017-03-09 13:09:40" "2017-03-13 13:55:36" "2017-03-14 11:25:24" "2017-03-14 11:42:57" ...
## $ pw_profile_form_fill_time : chr "2017-03-09 13:19:44" "2017-03-13 14:10:34" "2017-03-14 11:35:15" "2017-03-14 11:53:46" ...
## $ pw_profile_reg_date : chr "2017-03-09 13:20:08" "2017-03-13 14:10:58" "2017-03-14 11:35:39" "2017-03-14 11:54:10" ...
## $ pw_profile_fetal_demise : chr NA NA NA NA ...
## $ pw_profile_fetal_demise_month : chr NA NA NA NA ...
## $ anm_visit_detail_id : int 362 616 631 638 738 855 1207 NA 1208 1412 ...
## $ pw_profile_id.1 : int 351 566 581 588 688 803 1147 NA 1148 1327 ...
## $ qrcode.1 : chr "NK-DVL-LHR-2017-A-076" "NK-DVL-LHR-2017-A-077" "NK-DVL-LHR-2017-A-078" "NK-DVL-LHR-2017-A-079" ...
## $ anm_visit_detail_preg_month : chr "9" "7" "5" "8" ...
## $ anm_visit_detail_anc_count : chr "5" "4" "3" "4" ...
## $ anm_visit_detail_headache : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ anm_visit_detail_bp_sys : int 110 110 120 120 115 98 130 NA 130 120 ...
## $ anm_visit_detail_bp_dia : int 70 70 80 80 78 66 90 NA 80 80 ...
## $ anm_visit_detail_pulse : int 83 74 72 80 72 72 74 NA 94 72 ...
## $ anm_visit_detail_weight : num 55 67 50 52 44 50 58 NA 59 37 ...
## $ anm_visit_detail_height : num 150 159 160 152 146 150 148 NA 158 152 ...
## $ anm_visit_detail_bmi : num 24.4 26.5 19.5 22.5 20.6 ...
## $ anm_visit_detail_temp : num 98 98 98 97 98 98 98 NA 98 97 ...
## $ anm_visit_detail_hb : num 9 9.5 11.5 9.4 10.5 10 10 NA 11 12 ...
## $ anm_visit_detail_breathlessness : num 0 0 0 0 0 0 0 NA 0 0 ...
## $ anm_visit_detail_urine : num 0 0 0 0 0 0 0 NA 0 0 ...
## $ anm_visit_detail_abdomen_pain : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ anm_visit_detail_edema : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ anm_visit_detail_body_edema : int 0 0 0 0 0 0 0 NA 0 0 ...
## $ anm_visit_detail_fetal_bpm : int 148 0 140 140 140 145 142 NA 0 140 ...
## $ anm_visit_detail_gps_lat : num NA NA NA NA NA NA NA NA NA NA ...
## $ anm_visit_detail_gps_long : num NA NA NA NA NA NA NA NA NA NA ...
## $ anm_visit_detail_form_id : chr "6e9d9846fe3c0c2e_2017-03-09 13:19:44" "6e9d9846fe3c0c2e_2017-03-13 14:10:34" "6e9d9846fe3c0c2e_2017-03-14 11:35:15" "6e9d9846fe3c0c2e_2017-03-14 11:53:46" ...
## $ user_id.2 : chr "238" "238" "238" "238" ...
## $ anm_visit_detail_form_start_time : chr "2017-03-09 13:09:40" "2017-03-13 13:55:36" "2017-03-14 11:25:24" "2017-03-14 11:42:57" ...
## $ anm_visit_detail_form_fill_time : chr "2017-03-09 13:19:44" "2017-03-13 14:10:34" "2017-03-14 11:35:15" "2017-03-14 11:53:46" ...
## $ anm_visit_detail_followup_date : chr "2017-03-28" "2017-04-11" "2017-04-11" "2017-04-04" ...
## $ anm_visit_detail_receive_time : chr "2017-03-09 13:20:08" "2017-03-13 14:10:58" "2017-03-14 11:35:39" "2017-03-14 11:54:10" ...
## $ system_risk_reason : chr NA "PREVIOUS C-SECTION" NA NA ...
## $ risk_status : chr "1" "3" "1" "1" ...
## $ first_record : chr "1" "1" "1" "1" ...
## $ anm_visit_detail_edd_by_sonography: chr NA NA NA NA ...
block3$user_id <- as.numeric(block3$user_id)
## Warning: NAs introduced by coercion
#Following variables represent categories and not character or intgers
categorical <- c("user_type", "user_sex", "user_acc_status", "pw_profile_bpl_card", "pw_profile_edu", "pw_profile_type_uid",
"pw_profile_first_preg", "pw_profile_birth_order", "pw_profile_prev_pih",
"pw_profile_prev_sb", "pw_profile_prev_low_wt", "pw_profile_prev_miscarriage",
"pw_profile_prev_c_section", "pw_profile_prev_pph", "pw_profile_heart_disease",
"pw_profile_thyroid_disease", "pw_profile_epilepsy", "pw_profile_reproductive_surg",
"pw_profile_other_flag", "pw_profile_family_diabetes", "pw_profile_family_pih",
"pw_profile_multi_preg", "pw_profile_blood_group", "pw_profile_hiv")
#Converting to categorical variables
block3[, categorical] <- lapply(block3[, categorical], as.factor)
str(block3[, categorical])
## 'data.frame': 3622 obs. of 24 variables:
## $ user_type : Factor w/ 19 levels " ANAEMIA"," HIGH BLOOD PRESSURE",..: 5 5 5 5 5 5 5 NA 5 5 ...
## $ user_sex : Factor w/ 4 levels " ANAEMIA","0000-00-00",..: 3 3 3 3 3 3 3 NA 3 3 ...
## $ user_acc_status : Factor w/ 1 level "1": 1 1 1 1 1 1 1 NA 1 1 ...
## $ pw_profile_bpl_card : Factor w/ 8 levels "0","1","7057668856",..: 2 1 1 1 1 1 1 NA 1 2 ...
## $ pw_profile_edu : Factor w/ 8 levels "0","1","2","3",..: 2 6 4 7 4 5 5 NA 3 4 ...
## $ pw_profile_type_uid : Factor w/ 5 levels "1","2","3","4",..: 4 2 4 4 2 2 4 NA 2 4 ...
## $ pw_profile_first_preg : Factor w/ 8 levels "0","1","2017-01-09",..: 1 1 1 2 2 2 2 NA 1 1 ...
## $ pw_profile_birth_order : Factor w/ 8 levels "0","1","2","2017-10-16",..: 3 2 3 NA NA NA NA NA 2 2 ...
## $ pw_profile_prev_pih : Factor w/ 4 levels "0","1","2","7": 1 1 1 NA NA NA NA NA 1 1 ...
## $ pw_profile_prev_sb : Factor w/ 3 levels "0","1","7": 1 1 1 NA NA NA NA NA 1 1 ...
## $ pw_profile_prev_low_wt : Factor w/ 3 levels "0","1","7": 1 1 1 NA NA NA NA NA 1 1 ...
## $ pw_profile_prev_miscarriage : Factor w/ 3 levels "0","1","7": 1 1 1 NA NA NA NA NA 1 1 ...
## $ pw_profile_prev_c_section : Factor w/ 3 levels "0","1","7": 1 2 1 NA NA NA NA NA 1 1 ...
## $ pw_profile_prev_pph : Factor w/ 3 levels "0","1","7": 1 1 1 NA NA NA NA NA 1 1 ...
## $ pw_profile_heart_disease : Factor w/ 2 levels "0","9": 1 1 1 1 1 1 1 NA 1 1 ...
## $ pw_profile_thyroid_disease : Factor w/ 4 levels "0","1","7","9": 1 1 1 1 1 1 2 NA 1 1 ...
## $ pw_profile_epilepsy : Factor w/ 4 levels "0","1","7","9": 1 1 1 1 1 1 1 NA 1 1 ...
## $ pw_profile_reproductive_surg: Factor w/ 3 levels "0","1","9": 1 1 1 1 1 1 1 NA 1 1 ...
## $ pw_profile_other_flag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 NA 1 1 ...
## $ pw_profile_family_diabetes : Factor w/ 3 levels "0","1","9": 1 1 1 1 1 1 1 NA 1 1 ...
## $ pw_profile_family_pih : Factor w/ 3 levels "0","1","9": 1 1 1 1 1 1 1 NA 1 1 ...
## $ pw_profile_multi_preg : Factor w/ 5 levels "0","2","3","7",..: 1 1 1 1 1 1 1 NA 5 1 ...
## $ pw_profile_blood_group : Factor w/ 10 levels "0","1","2","3",..: 5 5 5 5 2 3 4 NA 3 3 ...
## $ pw_profile_hiv : Factor w/ 6 levels "0","1","2","4",..: 1 1 1 1 1 1 1 NA 6 1 ...
#Following are date variables and should be changed to date
datevars <- c("pw_profile_lmp", "pw_profile_edd")
block3[, datevars] <- lapply(block3[, datevars], ymd)
## Warning: 8 failed to parse.
## Warning: 2 failed to parse.
str(block3[, datevars])
## 'data.frame': 3622 obs. of 2 variables:
## $ pw_profile_lmp: Date, format: "2016-07-06" "2016-08-21" ...
## $ pw_profile_edd: Date, format: "2017-04-12" "2017-05-28" ...
block3$pw_profile_dob <- dmy(block3$pw_profile_dob)
## Warning: All formats failed to parse. No formats found.
#Following are datetime variables and should be changed to datetime
datetimevars <- c("registeration_date", "last_login_time", "pw_profile_form_start_time", "pw_profile_form_fill_time",
"pw_profile_reg_date")
block3[, datetimevars] <- lapply(block3[, datetimevars], ymd_hms)
## Warning: 8 failed to parse.
## Warning: 2 failed to parse.
str(block3[, datetimevars])
## 'data.frame': 3622 obs. of 5 variables:
## $ registeration_date : POSIXct, format: "2017-02-18 11:42:18" "2017-02-18 11:42:18" ...
## $ last_login_time : POSIXct, format: "2017-09-30 16:21:07" "2017-09-30 16:21:07" ...
## $ pw_profile_form_start_time: POSIXct, format: "2017-03-09 13:09:40" "2017-03-13 13:55:36" ...
## $ pw_profile_form_fill_time : POSIXct, format: "2017-03-09 13:19:44" "2017-03-13 14:10:34" ...
## $ pw_profile_reg_date : POSIXct, format: "2017-03-09 13:20:08" "2017-03-13 14:10:58" ...
#Reading case closed files
pw_case_flag <- read.csv("pw_case_flag_05_apr.csv", stringsAsFactors = FALSE, header = TRUE,
na.strings=c("NULL","NULL "," NULL ","NA"," ","", "-1"))
#Select profiles whose cases are closed
pw_closed <- sqldf("select distinct(pw_profile_id) from pw_case_flag where pw_case_flag_closed = 1")
#Removing closed profiles from block3
block3_1 <- sqldf("select * from block3 where pw_profile_id not in pw_closed")
#Removing user ids less than 10
block3_1 <- block3_1[(which(block3_1$user_id > 10)),]
agg_user <- block3_1 %>% group_by(user_id) %>% tally()
usr_lines <- ggplot(agg_user, aes(x = user_id, y = n)) +
geom_line() + xlab("User Id") + ylab("Number of Records")
ggplotly(usr_lines)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#Aggregate number of records by each user
usr_points <- ggplot(agg_user, aes(x = user_id, y = n)) +
geom_point() + xlab("User Id") + ylab("Number of Records")
ggplotly(usr_points)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
block3_1$anm_visit_detail_form_fill_date <- as.Date(block3_1$anm_visit_detail_form_fill_time)
#Form filling trend by each user
agg_user_date <- block3_1 %>% group_by(user_id, anm_visit_detail_form_fill_date) %>% tally()
agg_user_date <- agg_user_date[!is.na(agg_user_date$anm_visit_detail_form_fill_date),]
usrid <- sort(unique(na.omit(agg_user_date$user_id)))
#Show form filling trend for each of the users
for(i in seq_len(length(usrid))){
plot <- ggplot(agg_user_date, aes(x = anm_visit_detail_form_fill_date, y = n)) +
geom_point() + xlab("Date") + ylab("Number of records") +
ggtitle(paste("User id", as.character(usrid[i]))) +
facet_wrap_paginate(~user_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
#print(ggplotly(plot))
print(plot)
}
#Aggregate values by each subcenter
agg_subcent <- block3_1 %>% group_by(subcenter_id)
agg_subcent_pw <- agg_subcent %>% group_by(pw_profile_id)
pw_profiles <- sort(unique(na.omit(agg_subcent_pw$pw_profile_id)))
subcent <- sort(unique(na.omit(agg_subcent$subcenter_id)))
#Show systolic bp values in each of the subcenters
#for(i in seq_len(length(pw_profiles))){
for(i in seq_len(1:10)){
plot <- ggplot(agg_subcent_pw, aes(x = anm_visit_detail_anc_count, y = anm_visit_detail_bp_sys)) +
geom_point() + xlab("ANM visit number") + ylab("Blood Pressure Systolic") +
ggtitle(paste("PW Profile", as.character(pw_profiles[i])))
#facet_wrap_paginate(~pw_profile_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
## Warning in seq_len(1:10): first element used of 'length.out' argument
ggplot(agg_subcent_pw[agg_subcent_pw$pw_profile_id == 6808,], aes(x = anm_visit_detail_anc_count, y = anm_visit_detail_bp_sys)) +
geom_point() + xlab("ANM visit number") + ylab("Blood Pressure Systolic")
#Show diastolic bp values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_bp_dia)) + geom_point() + xlab("Pw Profile Id") + ylab("Blood Pressure Diastolic") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#Show pulse values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_pulse)) +
geom_point() + xlab("Pw Profile Id") + ylab("Pulse rate") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#Show weight values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_weight)) +
geom_point() + xlab("Pw Profile Id") + ylab("Weight") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#Show height values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_height)) +
geom_point() + xlab("Pw Profile Id") + ylab("Height") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#Show bmi values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_bmi)) +
geom_point() + xlab("Pw Profile Id") + ylab("BMI") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#Show temperature values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_temp)) +
geom_point() + xlab("Pw Profile Id") + ylab("Temperature") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#Show Haemoglobin count values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_hb)) +
geom_point() + xlab("Pw Profile Id") + ylab("Hb count") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#Show Fetal bpm values in each of the subcenters
for(i in seq_len(length(subcent))){
plot <- ggplot(agg_subcent, aes(x = pw_profile_id, y = anm_visit_detail_fetal_bpm)) +
geom_point() + xlab("Pw Profile Id") + ylab("Fetal bpm") +
ggtitle(paste("Subcenter", as.character(subcent[i]))) +
facet_wrap_paginate(~subcenter_id,nrow = 1, ncol = 1, scales = "fixed", page = i)
print(plot)
}
#-----Center wise records gainst target number of records------------------
target_anc <- read.csv("Deola_block3_target.csv", skip = 1, header = TRUE, stringsAsFactors = FALSE,
na.strings=c("NULL","NULL "," NULL ","NA"," ","", "-1"))
subcenter_details <- read.csv("subcenter.csv", header = TRUE, stringsAsFactors = FALSE,
na.strings=c("NULL","NULL "," NULL ","NA"," ","", "-1"))
target_anc1 <- target_anc %>% left_join(subcenter_details, by = c("Sub.Center.Name"="subcenter_name"))
total_subcent <- agg_subcent %>% tally()
total_subcent1 <- merge(total_subcent, target_anc1, by = "subcenter_id", all.x = TRUE)
ggplot(total_subcent1, aes(x = subcenter_id, y = n)) +
geom_bar(stat = "identity") + xlab("Subcenter Id") + ylab("Number of Records") +
ggtitle("Subcenter-wise record collection") +
geom_line(aes(y = Expected.ANCs.in.a.year))
total_subcent2 <- total_subcent1 %>% gather(key = record, value = count, n, Expected.ANCs.in.a.year)
ggplot(total_subcent2, aes(x = subcenter_id, y = count, fill = record)) +
geom_bar(stat = "identity", position = "dodge") + xlab("Subcenter Id") + ylab("Number of Records") +
ggtitle("Subcenter-wise record collection")
#--------------Fetal bpm values-----------------------
fhr <- read.csv("fhr_limits.csv",header = TRUE, stringsAsFactors = FALSE,
na.strings=c("NULL","NULL "," NULL ","NA"," ","", "-1"))
agg_subcent_pw$anm_visit_detail_preg_month <- as.numeric(agg_subcent_pw$anm_visit_detail_preg_month)
## Warning: NAs introduced by coercion
agg_subcent_pw1 <- merge(agg_subcent_pw, fhr, by.x = "anm_visit_detail_preg_month", by.y = "month", all.x = TRUE) %>%
group_by(pw_profile_id) %>% arrange(pw_profile_id, anm_visit_detail_preg_month)
## Warning: package 'bindrcpp' was built under R version 3.3.3
agg_subcent_pw2 <- agg_subcent_pw1 %>% gather(key = record, value = count, bpm_low, bpm_high)
ggplot(agg_subcent_pw2[agg_subcent_pw2$pw_profile_id == 6084,], aes(x = anm_visit_detail_preg_month, y = anm_visit_detail_fetal_bpm)) +
geom_point(na.rm = TRUE) + xlab("Pregnancy month") + ylab("Fetal Heart Rate") +
geom_line(aes(y = count, colour = record)) +
ggtitle("Fetal Heart Rate for PW Profile 6084")
